Through the theoretical adaptation of biometric technologies to people of variable abilities
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- Scope of the Study
- Rationale of the Study
- Glossary of Terms
Significance of the StudyThe technological underpinnings of biometric technologies are some have the most promising and life altering fundamentals in existence today. Barring cultural barriers, the adaptation and implementation of biometrics technologies could feasibly bring about a rudimentary shift with respect to security, access and control. Thereby, giving birth to the creation of many new assistive technology solutions and launching the world into a new era, an era where all things are possible and disabilities as we know them today have been eradicated from existence. Biometric technologies can be adapted to areas requiring secure access and control. Biometrics can be used to access logical assets and to potentially facilitate absolute control of both logical devices and physical components, in both the realities of the virtual and tangible worlds. In theory, biometric technologies could be adapted to interface with applications, personal computers, networks, accounts, human resource records, telephone system, automotive vehicles, planes, trains, wheelchairs, exoskeleton, and could be used in the invocation of customized profiles to enhance the mobility of people with varied ability levels (Nanavati et al.). An added benefit of to biometric technologies is that it could potentially provide society with a feasible resolution to one of the greatest challenges facing businesses of today. That problem is the task of business to maintain a qualified workforce. This is primarily because of the change from an industrial workforce to a knowledge workforce and because the baby boomers have only had about half as many children as their parents.
All attributes of the underlining technologies and the cultural barriers will include but not be limited to the positives and negatives of biometric readers, biometric characteristics, smart cards, neural interfaces, technology standards, implementation strategies, legal issues, privacy issues, barriers, workplace culture, government culture, civilian culture, the elderly, and people with disabilities (whom have the most to gain). Rationale of the StudyTo overcome the disenabling effects of mental, physical (mobility), structural (building), or emotional barriers as related to the access and control of electronic devices and technology that span the environments of both the virtual and tangible worlds. The societies of the world have hence, looked towards the advantages of assistive technologies for assistance. The reality of the matter is that while assistive can help to overcome many mental, physical, and emotional barriers it cannot and will not ever possess the ability to overcome the reigning number one barriers confronting people with disabilities. The reigning number one barrier has been created by the international society and is referred to as the cultural barriers. Cultural barriers embody numerous complex, dynamic, and diverse challenges to be overcome. These challenges are related to but are not limited to cultures of the workplaces and societies of the international communities (Hagner & DiLeo, 1993). Biometric technologies will play a significant role in the eradication of the threefold problem. However, the best rationale of all is that to do so is the mark of an enlighten people and the right thing to do. Glossary of TermsThe following are terms that will be used throughout the study. Ability to Verify/ATV: Is a combination of the FTE and FNMR. Abstract Interactor: An interactor that describes the selection, input, or output for a user interaction, without constraining the concrete form of the interaction. Accessibility: The opportunity for people of any ability level to interface with electronic devices or technology to overcome all logical and physical barriers. Acoustic Emission: A proprietary technique used in signature verification. As a user writes on a paper surface, the movement of the pen tip over the paper fibers generates acoustic emissions that are transmitted in the form of stress waves within the material of a writing block beneath the document being signed. The structure-borne elastic waves behave in materials in a similar way to sound waves in air and can be detected by a sensor attached to the writing block. Active Impostor Acceptance: When an impostor submits a modified, simulated or reproduced biometric sample, intentionally attempting to relate it to another person who is an enrollee, and the person is incorrectly identified or verified by a biometric system as being that enrollee. Compare with 'Passive Impostor Acceptance'. AFIS (Automated Fingerprint Identification System): A highly specialized biometric system that compares a single finger image with a database of finger images, AFIS is predominantly within law enforcement agencies. AIAP: Acronym for Alternate Interface Access Protocol. AIAP-URC: Acronym for Alternate Interface Access Protocol Universal Remote Console. Algorithm: A sequence of instructions that tell a biometric system how to solve a particular problem. An algorithm will have a finite number of steps and is typically used by the biometric engine to compute whether a biometric sample and template is a match. See also 'Artificial Neural Network'. Alternate/Abstract Interface Markup Language (AAIML): The Alternate & Abstract Interface Markup Language (AAIML) is a vehicle by which a target conveys an abstract user interface description to a URC in the control phase, i.e. after a session has been opened between the URC and the target. The abstract UI description is presentation independent and must include all features and functions the target provides via its default (built-in) user interface. API (Application Program Interface): A set of services or instructions used to standardize an application. An API is computer code used by an application developer. Any biometric system that is compatible with the API can be added or interchanged by the application developer. See also Part III Terms Related to Specific Biometric Techniques for 'SVAPI' under 'Speaker Verification'. Application Developer: An individual entrusted with developing and implementing a biometric application. Aqueous Humor: A transparent liquid contained in the anterior and posterior chambers of the eye, produced by the ciliary process it passes to the venous system via the canal of Schlemm. Artificial Neural Network: A method of computing a problem. An artificial neural network uses artificial intelligence to learn by past experience and compute whether a biometric sample and template is a match. See also 'Algorithm'. ASIC (Application Specific Integrated Circuit): An integrated circuit (silicon chip) that is specially produced for a biometric system to improve performance. Attempt: The submission of a biometric sample to a biometric system for identification or verification. A biometric system may allow more than one attempt to identify or verify. Authentication: Is the process of validating that an individual is in fact the person whom they claim to be. Auto-correlation: A proprietary finger scanning technique. Two identical finger images are overlaid in the auto-correlation process, so that light and dark areas, known as Moiré fringes, are created. Automatic ID/Auto ID: An umbrella term for any biometric system or other security technology that uses automatic means to check identity. This applies to both one-to-one verification and one-to-many identification. Backbone: The main wire of a network or the wire to which the nodes of a network connect. Behavioral Biometric: A biometric that is characterized by a behavioral trait that is learnt and acquired over time rather than a physiological characteristic. See Part III Terms Related to Specific Biometric Techniques for 'Keystroke Dynamics', 'Signature Verification' and 'Speaker Verification'. Contrast with 'Physical/Physiological Biometric'. Bifurcation: A branch made by more than one finger image ridge. Binning: A specialized technique used by some AFIS vendors. Binning is the process of classifying finger images according to finger image patterns. This predominantly takes place in law enforcement applications. Here finger images are categorized by characteristics such as arches, loops and whorls and held in smaller, separate databases (or bins) according to their category. Searches can be made against particular bins, thus speeding up the response time and accuracy of the AFIS search. Biometric: A measurable, physical characteristic or personal behavioral trait used to recognize the identity, or verify the claimed identity, of a living person. Biometric Application: The use to which a biometric system is put. See also 'Application Developer'. Biometric Data: The extracted information taken from the biometric sample and used either to build a reference template or to compare against a previously created reference template. Biometric Engine: The software element of the biometric system, which processes biometric data during the stages of enrolment and capture, extraction, comparison and matching. Biometric Identification Device: The preferred term is 'Biometric System'. Biometric Sample: Data representing a biometric characteristic of an end-user as captured by a biometric system. Biometric System: An automated system capable of, Capturing a biometric sample from an end user; Extracting biometric data from that sample; Comparing the biometric data with that contained in one or more reference templates; Deciding how well they match; and Indicating whether or not an identification or verification of identity has been achieved. Biometric Taxonomy: A method of classifying biometrics. For example, San Jose State University's (SJSU) biometric taxonomy uses partitions to classify the role of biometrics within a given biometric application. Thus an application may be classified as: Download 2.12 Mb. 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